Who reviews the reviewers? Who edits the editors? Many avenues for you to hold<i>BJSM</i>accountable
Bibliographic record
Abstract
Being open and transparent creates trust is one of the BMJ Publishing Group's nine values. Does the BJSM leadership team act openly and transparently to deserve the trust of our community? We put our case here but we leave the verdict to our valued readers and respected communities (including our 19 member societies who total over 12 000 subscribers). When we consider ‘ BJSM ’ we refer to the broad range of content that can be considered to start from bjsm.bmj.com. In addition to our 24 annual issues, BJSM is a dynamic, versatile contributor to health education and debate via >200 podcasts, 60 YouTube videos and >300 blog posts. We engage with a vast community on Facebook and Twitter—and these are two-way media where BJSM content can rapidly be tagged, trolled or trumpeted. In total, 4.5 million BJSM pages are viewed each year. If this volume of content did not generate debate and criticism it would be time to sack the Editorial team. Peer review of around 1200 papers/year …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.073 | 0.099 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".